CN111639034A - Test method, device, equipment and computer storage medium - Google Patents

Abstract

The invention relates to the technical field of financial technology (Fintech), and discloses a testing method. The method includes: determining a data input source according to an input business scenario type, and obtaining the first data input source in untrained label information of a knowledge map according to the data input source. a preset number of target label information; obtain the evaluation indicators corresponding to each target label information, and obtain the index influence factors corresponding to each evaluation index; calculate the result weight value corresponding to each target label information according to each evaluation index and each index impact factor, and correct the recommendation score value corresponding to each target label information according to the weight value of each result; determine a second preset number of target recommendation score values based on the corrected recommendation score values, and determine the target recommendation score value corresponding to each target recommendation score value to be evaluated. Scenarios are tested as test cases. The invention also discloses a testing device, equipment and a computer storage medium. The present invention improves the effectiveness of automated testing.

Description

Test method, apparatus, equipment and computer storage medium

technical field

The present invention relates to the technical field of testing of financial technology (Fintech), and in particular, to a testing method, apparatus, equipment and computer storage medium.

Background technique

With the development of computer technology, more and more technologies (big data, distributed, artificial intelligence, etc.) are applied in the financial field, and the traditional financial industry is gradually transforming into financial technology (Fintech). Real-time requirements also put forward higher requirements for technology. At present, when testers conduct various automated tests (such as use case regression testing), testers generally execute test cases manually based on their own experience, which is easily affected by the testers' own subjective consciousness, and there are scattered test concerns and no targeted testing. There are many problems such as lack of coverage of key business scenarios, easy to ignore key points, etc., resulting in low test effectiveness. Therefore, how to improve the effectiveness of automated testing has become an urgent technical problem to be solved.

SUMMARY OF THE INVENTION

The main purpose of the present invention is to propose a testing method, device, equipment and computer storage medium, aiming at solving the technical problem of how to improve the effectiveness of automated testing.

In order to achieve the above object, the present invention provides a kind of test method, and the test method comprises the following steps:

Determine a data input source according to the input business scenario type, and obtain a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source;

obtaining the evaluation index corresponding to each of the target label information, and obtaining the index impact factor corresponding to each of the evaluation indexes;

Calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and correct the recommendation score value corresponding to each of the target label information according to each of the result weight values;

A second preset number of target recommendation score values are determined based on each of the corrected recommendation score values, and the to-be-evaluated solution corresponding to each of the target recommendation score values is tested as a test case.

Optionally, the step of calculating the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors includes:

Calculate the total value of the revised index of the to-be-evaluated scheme corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator impact factors;

A result weight value corresponding to each of the target label information is calculated according to the total value of each of the correction indicators and each of the evaluation indicators.

Optionally, the step of correcting the recommended score value of the to-be-evaluated scheme corresponding to each of the target label information according to each of the result weight values includes:

Obtain the recommendation score value of the to-be-evaluated scheme corresponding to each of the target label information, traverse each of the recommended score values in turn, and determine the current result weight value corresponding to the currently traversed current recommendation score value in each of the result weight values;

The current recommendation score value and the current result weight value are calculated based on a preset calculation formula, and the current recommendation score value is corrected according to the calculation result until the traversal of each recommendation score value is completed.

Optionally, the step of obtaining the recommended score value of the to-be-evaluated scheme corresponding to each of the target label information includes:

Traverse the to-be-evaluated schemes corresponding to the target label information in turn, determine the target library of the currently traversed current to-be-evaluated scheme in the knowledge graph, obtain the default score value of the target library, and use the default score value as The recommended score value of the currently to-be-evaluated scheme until the traversal of each of the to-be-evaluated schemes is completed.

Optionally, the step of obtaining the evaluation index corresponding to each of the target label information includes:

Traverse each of the target label information in turn, and determine that the current target label information currently traversed corresponds to a plurality of different types of indicator values, and use each of the indicator values as the evaluation indicators corresponding to the current target label information, until each The target tag information traversal is completed.

Optionally, the step of obtaining the index impact factor corresponding to each of the evaluation indexes includes:

Traverse each of the evaluation indicators in turn, determine all the indicator values in the current evaluation indicators currently traversed, and determine the indicator impact factor corresponding to the current evaluation indicators according to the indicator values, until the traversal of each evaluation indicator is completed. .

Optionally, the step of obtaining a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source includes:

Acquire a plurality of untrained label information in the knowledge graph, and sort each of the untrained label information according to the data input source;

A first preset number of target tag information is acquired from each of the untrained tag information according to the sorting result of the sorting.

In addition, in order to achieve the above purpose, the present invention also provides a test device, the test device includes:

a determination module, configured to determine a data input source according to the input business scenario type, and obtain a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source;

an acquisition module, configured to acquire the evaluation indicators corresponding to each of the target label information, and acquire the index impact factors corresponding to each of the evaluation indicators;

The correction module is configured to calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and to assign a recommendation score corresponding to each of the target label information according to each of the result weight values. value is corrected;

A test module, configured to determine a second preset number of target recommendation score values based on the corrected recommendation score values, and use the to-be-evaluated solutions corresponding to the target recommendation score values as test cases for testing.

In addition, in order to achieve the above object, the present invention also provides a test device, the test device includes: a memory, a processor, and a test program stored on the memory and executable on the processor, the test program The steps of the testing method as described above are implemented when executed by the processor.

In addition, in order to achieve the above object, the present invention also provides a computer storage medium, where a test program is stored on the computer storage medium, and when the test program is executed by a processor, the steps of the above test method are implemented.

The present invention determines the data input source according to the input business scenario type, and obtains a first preset number of target label information in the untrained label information of the knowledge map according to the data input source; and obtains the corresponding target label information. Evaluate indicators, and obtain the indicator impact factors corresponding to each of the evaluation indicators; calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator impact factors, and according to each of the result weight values Correct the recommendation score values corresponding to each of the target label information; determine a second preset number of target recommendation score values based on the corrected recommendation score values, and determine the target recommendation score values corresponding to each of the target recommendation score values to be evaluated Scenarios are tested as test cases. By determining the data input source according to the type of business scenario, and obtaining the target label information in the knowledge graph, the result weight value is calculated according to the evaluation index and index impact factor corresponding to the target label information, and the recommendation score value corresponding to the target label information is corrected. , and then determine the test case for testing according to the corrected recommended score value, so that the test case that conforms to the business scenario type can be effectively selected for testing, avoiding the manual selection of test cases for testing in the prior art, resulting in effective testing. The phenomenon of reduced performance occurs, which improves the effectiveness and accuracy of automated testing.

Description of drawings

1 is a schematic structural diagram of a test equipment of a hardware operating environment involved in an embodiment of the present invention;

Fig. 2 is the schematic flow chart of the first embodiment of the testing method of the present invention;

Fig. 3 is the device module schematic diagram of the test device of the present invention;

4 is a schematic diagram of the recommended score of the knowledge graph in the testing method of the present invention;

Fig. 5 is the test flow schematic diagram in the test method of the present invention;

6 is a schematic diagram of the construction of a knowledge graph in the testing method of the present invention.

The realization, functional characteristics and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed ways

It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

As shown in FIG. 1 , FIG. 1 is a schematic structural diagram of a test device of a hardware operating environment involved in the solution of an embodiment of the present invention.

The test device in the embodiment of the present invention may be a PC or a server device, and a Java virtual machine runs thereon.

As shown in FIG. 1 , the test equipment may include: a processor 1001 , such as a CPU, a network interface 1004 , a user interface 1003 , a memory 1005 , and a communication bus 1002 . Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. Optionally, the network interface 1004 may include a standard wired interface and a wireless interface (eg, a WI-FI interface). The memory 1005 may be high-speed RAM memory, or may be non-volatile memory, such as disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001 .

Those skilled in the art can understand that the structure of the test equipment shown in FIG. 1 does not constitute a limitation on the equipment, and may include more or less components than the one shown, or combine some components, or arrange different components.

As shown in FIG. 1 , the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module and a test program.

In the test equipment shown in FIG. 1, the network interface 1004 is mainly used to connect to the background server, and perform data communication with the background server; the user interface 1003 is mainly used to connect the client (client), and perform data communication with the client; and the processing The tester 1001 can be used to call the test program stored in the memory 1005 and perform the operations in the test method described below.

Based on the above hardware structure, an embodiment of the testing method of the present invention is proposed.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of the first embodiment of the testing method of the present invention, and the method includes:

Step S10, determining a data input source according to the input business scenario type, and obtaining a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source;

In this embodiment, GIT (distributed version control system) is used for version management during testing, and it is integrated into JENKINS (open source software project). Then call the preset quality middle platform (optionally represented by TctpTest) platform CI (Continuous Integration, continuous integration) interface for automatic version deployment, that is, first determine the type of business scenario according to the information input by the developer or tester, and request the knowledge graph Get the recommended use case label information under this business scenario type. After obtaining the feedback information fed back by the knowledge graph, all test cases to be tested are determined according to the feedback information, and then all test cases are directly executed through the test execution engine. And after the test execution engine is executed, it will output the corresponding execution results and store them in the database, so that the TctpTest platform can backtrack through the data reports with the execution results in the database, making the test more efficient and able to Reduce the miss rate.

Among them, the knowledge graph includes three functions. The first part is to generate an efficient and valuable benchmark label case library through multi-dimensional modeling. The second part is the interface interaction with the TctpTest platform. The interface can include: requesting recommended labels, querying more labels (using paging and query fields in the DB (database) table to create indexes, avoiding too much label information, reducing bandwidth usage, improving Query performance, support fuzzy matching, find the label information that the executor wants as soon as possible), feedback label information (push interface), execute specified label test cases, etc. The third part is that the label case information executed this time can be used as a new data input source to re-participate in the modeling algorithm to obtain more accurate label weights.

Therefore, in this embodiment, the input business scenario type can be obtained first, and the parent function type and child function type associated with the business scenario type can be obtained in the database to form a data input source. That is, the data input source includes the business scenario type, the parent function type, and the child function type. Among them, the parent function type can be the large requirement of the test case. The child functional type can be a small requirement under a large requirement. For example, the account opening account is the parent function type, and the card information cardinfo (card information table) under the account opening can be the child function type.

Therefore, after obtaining the data input source, you can obtain the training data (that is, a certain amount of untrained training data) with the flag of 0 in the knowledge graph according to the data input source, and determine the business scenario type according to the training data. The training case label information (that is, the untrained label information), and then use the parent function type + the number of times of use of the child function type in the data input source to sort the untrained label information, and select and use it based on the sorting result. The untrained label information corresponding to the maximum value of the first N times (ie, the first preset number) is used as the target label information. That is, it is necessary to modify the score (recommended score value) value corresponding to the target label information.

Step S20, obtaining evaluation indexes corresponding to each of the target label information, and obtaining an index impact factor corresponding to each of the evaluation indexes;

After obtaining the target label information, the cases corresponding to the target label information can be used as the solutions to be evaluated, and the actual usage times of the TctpTest platform can be ranked according to the database of the cases to be evaluated, business scenarios, and test expert experience points. Evaluation metrics, namely:

其中，a_ij表示为第i个测试案例中第j指标的量值。

Among them, a _ij represents the magnitude of the jth index in the ith test case.

And in this embodiment, according to the importance occupied by each evaluation index, the index influence factor is given, namely:

其中，

b_i表示为第i个评估指标影响因子。

in,

b _i is expressed as the impact factor of the i-th evaluation index.

And because the knowledge graph includes knowledge public library, BUG experience library, specific business scenario library and training database. Among them, as shown in Figure 4, the knowledge common library includes the most basic use cases. Generally, the main process must be executed in this business scenario by default. Therefore, the score (recommended score value) of the knowledge common library can be set to the maximum value MAX. Since the BUG experience library is only carried out in very few scenarios, the score of the BUG experience library can be set to the median MID. The specific business scenario library is only for some specific specific business scenarios, so the score of the specific business scenario library can be set to the minimum value MIN. Moreover, the training database will only affect the score value of the BUG experience database and the specific business scenario database, but will not affect the score value of the knowledge public database. And in this embodiment, the label use case recommendation for each test execution depends on the size of the score, and the larger the score is, the higher the priority is to be recommended. Therefore, when calculating the evaluation index, it is necessary to detect the database where the case of the solution to be evaluated is located, so as to determine the evaluation index based on the score.

Step S30: Calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and perform a recommendation score value corresponding to each of the target label information according to each of the result weight values. Correction;

After each evaluation index and each index influence factor are obtained, the result weight value corresponding to each target label information can be calculated, and then the recommendation score value of the to-be-evaluated scheme corresponding to each target label information can be corrected according to each result weight value. It should be noted that, a result weight value only corrects the recommendation score value of the to-be-evaluated scheme associated with the result weight value, and does not change other recommendation score values.

That is, get a new matrix first:

Among them, c _i is expressed as the total value of the revised index of the i-th test case, and the formula for calculating the result weight value in the i-th test case:

When each result weight value is obtained and the recommendation score value is modified according to the result weight value, the corrected recommendation score value can be determined according to the formula score=score*(1+p). The score is the recommendation score value, and the corrected recommendation score value cannot exceed the default maximum score value in the knowledge graph. And after correcting the recommendation score value, the training data of the target label information corresponding to the corrected recommendation score value will be set to 1 (that is, it is determined that the processing has been performed).

Step S40: Determine a second preset number of target recommendation score values based on the corrected recommendation score values, and use the to-be-evaluated solutions corresponding to the target recommendation score values as test cases for testing.

After the corrected recommended score values are obtained, the corrected recommended score values can be sorted in descending order, and a second preset number of recommended score values greater than a certain value is selected. The recommended score value (that is, the target recommended score value), and the to-be-evaluated solution corresponding to the second preset number of recommended score values is placed as a test case in the test execution engine for testing, and after the test is completed, the test result will be actively stored in the database.

In addition, in order to assist the understanding of the test flow in this embodiment, an example is given below.

For example, as shown in Figure 5, developers or testers determine the type of business scenario in the Tctp Test platform, and request a recommended label from the knowledge graph according to the type of business scenario, and the knowledge graph will model and filter out suitable recommendations label, and return the recommended label to the Tctp Test platform. The Tctp Test platform will return the case corresponding to the recommended label according to the knowledge graph to execute in the test execution engine, that is, the test execution engine requests to execute the corresponding case (ie test case), and the Tctp Test platform will feedback information (ie case execution) to the knowledge graph. After the test execution engine executes the test case, it will store the results in the DB (database) while outputting the results, so that the knowledge graph can be called.

Among them, the architectural composition of the knowledge graph can be shown in Figure 6, including the knowledge public database, the BUG experience database, the business scenario database and the training database. Among them, the business scene library includes scene type type, parent function type, child function type and score value, and the modeling in the knowledge graph is based on the SCORE value of the business scene library, that is, from nearly M training trees (approximately M*100 test cases) to select N with the highest actual usage, and the usage rates of different intervals correspond to different SCORE weights. And when modeling in the knowledge graph, the data input source (including type, parent type and child type) will be obtained from the training database for modeling. The knowledge base includes the most basic use cases, such as account opening, login, etc. The BUG experience library is a case library for the representative BUG found in production and summarizing the precipitation output. The business scenario library is a test case collection library for scenarios involved in specific projects. The training database is a repository of use case label information finalized by the performer each time on the Tctp Test platform. In addition, the public knowledge base, the BUG experience base and the business scenario base are a collection of case bases that are directly oriented and provided to test executors, and can be regularly maintained and updated by testers. The training database is the underlying engine library, which is used to input the information of the label through the actual test to input modeling training, and feed back the obtained test guidance label to the other three libraries to form a closed loop of the knowledge map. And knowledge fusion will be carried out in the knowledge graph, that is, for the case failure sign, duplicate cases will be automatically identified and excluded. Moreover, knowledge storage can be performed in the knowledge graph, that is, a key index based on business scenario type + parent function type can be established to speed up query.

In this embodiment, a data input source is determined according to the input business scenario type, and a first preset number of target label information is obtained from the untrained label information of the knowledge graph according to the data input source; each target label information is obtained; The evaluation index corresponding to the label information, and the index influence factor corresponding to each evaluation index is obtained; the result weight value corresponding to each target label information is calculated according to each evaluation index and each index influence factor, and the result weight value corresponding to each target label information is calculated according to each evaluation index. The result weight value is used to correct the recommendation score value corresponding to each of the target label information; a second preset number of target recommendation score values are determined based on each of the corrected recommendation score values, and each of the target recommendation score values The corresponding scheme to be evaluated is tested as a test case. By determining the data input source according to the type of business scenario, and obtaining the target label information in the knowledge graph, the result weight value is calculated according to the evaluation index and index impact factor corresponding to the target label information, and the recommendation score value corresponding to the target label information is corrected. , and then determine the test case for testing according to the corrected recommended score value, so that the test case that conforms to the business scenario type can be effectively selected for testing, avoiding the manual selection of test cases for testing in the prior art, resulting in effective testing. The phenomenon of reduced performance occurs, which improves the effectiveness and accuracy of automated testing.

Further, based on the first embodiment of the testing method of the present invention, a second embodiment of the testing method of the present invention is proposed. This embodiment is the step S30 of the first embodiment of the present invention, and the refinement of the step of calculating the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors includes:

Step a, according to each described evaluation index and each described index influence factor, calculate the revised index total value of the to-be-evaluated scheme corresponding to each described target label information;

In this embodiment, after each evaluation index and the index impact factor corresponding to each evaluation index are obtained, the total value of the revised index of the to-be-evaluated scheme corresponding to each target label information can be calculated by using a calculation formula set in advance. Its calculation formula can be:

Among them, ci represents the total value of the revised index (ie, the total value of the revised index) of the _i -th test case.

Step b: Calculate the result weight value corresponding to each of the target label information according to the total value of each of the correction indicators and each of the evaluation indicators.

After the total value of the correction index of each scheme to be evaluated is obtained, the result weight value corresponding to each target label information can be sequentially calculated according to the total value of each correction index and each evaluation index. Its calculation formula can be:

In this embodiment, the total value of the correction index is calculated according to each evaluation index and each index influence factor, and the result weight value is calculated according to the total value of the correction index, thereby ensuring the accuracy of the calculated result weight value.

Further, the step of correcting the recommended score value of the to-be-evaluated scheme corresponding to each of the target label information according to each of the result weight values includes:

Step c, obtaining the recommendation score value of the to-be-evaluated scheme corresponding to each of the target label information, and traversing each of the recommended score values in turn, and determining the current result corresponding to the currently traversed current recommendation score value in each of the result weight values Weights;

In this embodiment, after obtaining each result weight value, it is also necessary to obtain the recommendation score value of the to-be-evaluated scheme corresponding to each target label information in the knowledge graph, and traverse each recommendation score value in turn to determine the current traversed current recommendation score value, and then obtain the result weight value corresponding to the current recommendation score value (ie, the current result weight value) in each result weight value.

Step d, calculate the current recommendation score value and the current result weight value based on a preset calculation formula, and correct the current recommendation score value according to the calculation result until the traversal of each recommendation score value is completed.

After the current recommendation score value and the current result weight value are obtained, the current recommendation score value can be corrected according to the preset calculation formula. The preset calculation formula may be score=score*(1+p), score is the current recommendation score value, and p is the current result weight value. It should be noted that each recommendation score value is corrected in the same way until the traversal of each recommendation score value is completed.

In this embodiment, the current result weight value is determined among the respective result weight values according to the current recommendation score value, the calculation is performed according to the current recommendation score value and the current result weight value, and the current recommendation score value is corrected based on the calculation result, thereby The accuracy of the correction of the current recommended score value is guaranteed.

Specifically, the step of obtaining the recommendation score value of the to-be-evaluated scheme corresponding to each of the target label information includes:

Step e, traverse the to-be-evaluated schemes corresponding to each of the target label information in turn, determine the target library of the currently traversed current to-be-evaluated schemes in the knowledge graph, obtain the default score value of the target library, and set the default The score value is used as the recommended score value of the current solution to be evaluated until the traversal of each solution to be evaluated is completed.

When obtaining each recommendation score value, it is possible to first traverse the to-be-evaluated schemes corresponding to each target tag information, and determine the target library of the currently traversed current to-be-evaluated schemes in the knowledge graph. The target library may be any one of a knowledge public library, a BUG experience library, and a specific business scenario library in the knowledge graph. And in the knowledge graph, a default recommendation score value will be set for each library. Therefore, after the target library is determined, the default score value of the target library (ie, the default recommended score value) can be obtained, and the default score value can be used as the recommended score value of the current scheme to be evaluated. Until the traversal of each to-be-evaluated scheme is completed, that is, each to-be-evaluated scheme is determined to have a recommended score value.

In this embodiment, by traversing each to-be-evaluated scheme, determining the target library currently to be evaluated and placed in the knowledge graph, and using the default score value of the target library as the recommended score value of the current to-be-evaluated scheme, until each to-be-evaluated scheme Traversal complete. Thus, the accuracy of the obtained recommendation score value of the scheme to be evaluated is guaranteed.

Further, the step of obtaining the evaluation index corresponding to each of the target label information includes:

Step f, traverse each of the target label information in turn, determine that the current target label information currently traversed corresponds to a plurality of index values of different types, and use each of the index values as an evaluation index corresponding to the current target label information, until The traversal of the target tag information is completed.

In this embodiment, when obtaining the evaluation index, it is necessary to traverse each target label information in turn, to determine the current target label information currently traversed, and to determine a plurality of different types of index values corresponding to the current target label information (such as in the case of the case). database, business scenarios, test expert experience points, ranking of the actual usage times of the Tctp Test platform, etc.), and use these indicators together as the evaluation indicators corresponding to the current target label information, until the traversal of each target label information is completed, that is, each target label information is obtained. The evaluation index corresponding to the target label information.

In this embodiment, by traversing each target tag information in turn, and using multiple index values corresponding to the currently traversed current target tag information as evaluation indicators corresponding to the current target tag information, until the traversal of each target tag information is completed, thereby ensuring the accuracy of the obtained evaluation indicators.

Further, the step of obtaining the index impact factor corresponding to each of the evaluation indexes includes:

Step h, traverse each of the evaluation indexes in turn, determine all the index values in the current evaluation indexes currently traversed, and determine the index influence factor corresponding to the current evaluation index according to the index values, until each evaluation The indicator traversal is complete.

After obtaining the evaluation index corresponding to the target label information, traverse each evaluation index in turn, and determine all the index values carried in the current evaluation index currently traversed, and determine the current evaluation index according to the importance of each index value to the current evaluation index. The index impact factor corresponding to the evaluation index. Until the traversal of each evaluation index is completed.

In this embodiment, by traversing each evaluation index and determining all index values in the current evaluation index, the index influence factor is determined until the traversal of each evaluation index is completed, thereby ensuring the accuracy of the obtained index influence factor.

Further, the step of obtaining a first preset number of target label information in the untrained label information of the knowledge graph according to the data input source includes:

Step m, obtaining a plurality of untrained label information in the knowledge graph, and sorting each of the untrained label information according to the data input source;

In this embodiment, it is necessary to take the training data (about M*100) whose flag is (untrained) for M times in the knowledge graph to obtain the training case label information (that is, the untrained label information) under the business scenario type, And sort each untrained label information according to the use times of parent function type + child function type in the data input source as keywords.

Step n: Obtain a first preset number of target label information from each of the untrained label information according to the sorting result of the sorting.

Then, according to the sorting result of the sorting, the first preset number of target tag information that is used more frequently is obtained from each untrained tag information.

In this embodiment, by sorting a plurality of untrained tag information in the knowledge graph according to the data input source, and obtaining a first preset number of target tag information according to the sorting result, the obtained target tag information is guaranteed to be accurate. accuracy.

The present invention also provides a test device, referring to FIG. 3 , the test device includes:

A determination module A10, configured to determine a data input source according to the input business scenario type, and obtain a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source;

The obtaining module A20 is used for obtaining the evaluation index corresponding to each of the target label information, and obtaining the index influence factor corresponding to each of the evaluation indexes;

A correction module A30, configured to calculate a result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and to recommend recommendations corresponding to each of the target label information according to each of the result weight values The score value is corrected;

The testing module A40 is configured to determine a second preset number of target recommendation score values based on each of the corrected recommendation score values, and use the to-be-evaluated solutions corresponding to each of the target recommendation score values as test cases for testing.

Optionally, the correction module A30 is also used for:

Calculate the total value of the revised index of the to-be-evaluated scheme corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator impact factors;

A result weight value corresponding to each of the target label information is calculated according to the total value of each of the correction indicators and each of the evaluation indicators.

Optionally, the correction module A30 is also used for:

Obtaining the recommendation score value of the to-be-evaluated scheme corresponding to each of the target tag information, traversing each of the recommended score values in turn, and determining the current result weight value corresponding to the currently traversed current recommendation score value in each of the result weight values;

The current recommendation score value and the current result weight value are calculated based on a preset calculation formula, and the current recommendation score value is corrected according to the calculation result until the traversal of each recommendation score value is completed.

Optionally, the correction module A30 is also used for:

Traverse the to-be-evaluated schemes corresponding to the target label information in turn, determine the target library of the currently traversed current to-be-evaluated scheme in the knowledge graph, obtain the default score value of the target library, and use the default score value as The recommended score value of the currently to-be-evaluated scheme until the traversal of each of the to-be-evaluated schemes is completed.

Optionally, the obtaining module A20 is also used for:

Traverse each of the target label information in turn, and determine that the current target label information currently traversed corresponds to a plurality of different types of indicator values, and use each of the indicator values as the evaluation indicators corresponding to the current target label information, until each The target tag information traversal is completed.

Optionally, the obtaining module A20 is also used for:

Traverse each of the evaluation indicators in turn, determine all the indicator values in the current evaluation indicators currently traversed, and determine the indicator impact factor corresponding to the current evaluation indicators according to the indicator values, until the traversal of each evaluation indicator is completed. .

Optionally, the determining module A10 is further configured to:

Acquire a plurality of untrained label information in the knowledge graph, and sort each of the untrained label information according to the data input source;

A first preset number of target tag information is acquired from each of the untrained tag information according to the sorting result of the sorting.

For the methods executed by the above program units, reference may be made to the various embodiments of the testing method of the present invention, which will not be repeated here.

The present invention also provides a computer storage medium.

A test program is stored on the computer storage medium of the present invention, and when the test program is executed by the processor, the steps of the above-mentioned test method are implemented.

For the method implemented when the test program running on the processor is executed, reference may be made to the various embodiments of the test method of the present invention, which will not be repeated here.

It should be noted that, herein, the terms "comprising", "comprising" or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article or system comprising a series of elements includes not only those elements, It also includes other elements not expressly listed or inherent to such a process, method, article or system. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article or system that includes the element.

The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages or disadvantages of the embodiments.

From the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course hardware can also be used, but in many cases the former is better implementation. Based on this understanding, the technical solutions of the present invention can be embodied in the form of software products in essence or the parts that make contributions to the prior art. The computer software products are stored in a storage medium (such as ROM/RAM) as described above. , magnetic disk, optical disk), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present invention.

The above are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the description and drawings of the present invention, or directly or indirectly applied in other related technical fields , are similarly included in the scope of patent protection of the present invention.

Claims (10)

1. a test method, is characterized in that, described test method comprises the steps: Determine a data input source according to the input business scenario type, and obtain a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source; obtaining the evaluation index corresponding to each of the target label information, and obtaining the index impact factor corresponding to each of the evaluation indexes; Calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and correct the recommendation score value corresponding to each of the target label information according to each of the result weight values; A second preset number of target recommendation score values are determined based on each of the corrected recommendation score values, and the to-be-evaluated solution corresponding to each of the target recommendation score values is tested as a test case. 2. The test method according to claim 1, wherein the step of calculating the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, comprises: Calculate the total value of the revised index of the to-be-evaluated scheme corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator impact factors; A result weight value corresponding to each of the target label information is calculated according to the total value of each of the correction indicators and each of the evaluation indicators. 3. The test method according to claim 1, wherein the step of correcting the recommended score value of the to-be-evaluated scheme corresponding to each of the target label information according to each of the result weight values comprises: Obtain the recommendation score value of the to-be-evaluated scheme corresponding to each of the target label information, traverse each of the recommended score values in turn, and determine the current result weight value corresponding to the currently traversed current recommendation score value in each of the result weight values; The current recommendation score value and the current result weight value are calculated based on a preset calculation formula, and the current recommendation score value is corrected according to the calculation result until the traversal of each recommendation score value is completed. 4. test method as claimed in claim 3, is characterized in that, the described step of obtaining the recommendation score value of the scheme to be evaluated corresponding to each described target label information, comprises: Traverse the to-be-evaluated schemes corresponding to the target label information in turn, determine the target library of the currently traversed current to-be-evaluated scheme in the knowledge graph, obtain the default score value of the target library, and use the default score value as The recommended score value of the currently to-be-evaluated scheme until the traversal of each of the to-be-evaluated schemes is completed. 5. The test method according to claim 1, wherein the step of obtaining the evaluation index corresponding to each of the target label information comprises: Traverse each of the target label information in turn, and determine that the current target label information currently traversed corresponds to a plurality of different types of indicator values, and use each of the indicator values as the evaluation indicators corresponding to the current target label information, until each The target tag information traversal is completed. 6. testing method as claimed in claim 1, is characterized in that, the described step of obtaining the index influence factor corresponding to each described evaluation index, comprises: Traverse each of the evaluation indicators in turn, determine all the indicator values in the current evaluation indicators currently traversed, and determine the indicator impact factor corresponding to the current evaluation indicators according to the indicator values, until the traversal of each evaluation indicator is completed. . 7. The test method according to any one of claims 1-6, wherein the step of obtaining a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source ,include: Acquire a plurality of untrained label information in the knowledge graph, and sort each of the untrained label information according to the data input source; A first preset number of target tag information is acquired from each of the untrained tag information according to the sorting result of the sorting. 8. A test device, characterized in that the test device comprises: a determination module, configured to determine a data input source according to the input business scenario type, and obtain a first preset number of target label information from the untrained label information of the knowledge graph according to the data input source; an acquisition module, configured to acquire the evaluation indicators corresponding to each of the target label information, and acquire the index impact factors corresponding to each of the evaluation indicators; The correction module is configured to calculate the result weight value corresponding to each of the target label information according to each of the evaluation indicators and each of the indicator influence factors, and to assign a recommendation score corresponding to each of the target label information according to each of the result weight values. value is corrected; A test module, configured to determine a second preset number of target recommendation score values based on the corrected recommendation score values, and use the to-be-evaluated solutions corresponding to the target recommendation score values as test cases for testing. 9. A test device, characterized in that the test device comprises: a memory, a processor, and a test program stored on the memory and executable on the processor, the test program being executed by the processor The steps of implementing the test method as claimed in any one of claims 1 to 7 when executed. 10. A computer storage medium, wherein a test program is stored on the computer storage medium, and when the test program is executed by a processor, the steps of the test method according to any one of claims 1 to 7 are realized .

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